Feature selection for regression problems based on the Morisita estimator of intrinsic dimension

Details

Feature selection for regression problems based on the Morisita estimator of intrinsic dimension

Journal

Pattern Recognition

Author(s)

GolayJ., LeuenbergerM., Kanevski M.

ISSN

0031-3203

Publication state

Published

Issued date

10/05/2017

Peer-reviewed

Oui

Volume

70

Pages

126-138

Language

english

Abstract

Data acquisition, storage and management have been improved, while the key factors of many phenomena are not well known. Consequently, irrelevant and redundant features artificially increase the size of datasets, which complicates learning tasks, such as regression. To address this problem, feature selection methods have been proposed. This paper introduces a new supervised filter based on the Morisita estimator of intrinsic dimension. It can identify relevant features and distinguish between redundant and irrelevant information. Besides, it offers a clear graphical representation of the results, and it can be easily implemented in different programming languages. Comprehensive numerical experiments are conducted using simulated datasets characterized by different levels of complexity, sample size and noise. The suggested algorithm is also successfully tested on a selection of real world applications and compared with RReliefF using extreme learning machine. In addition, a new measure of feature relevance is presented and discussed.